Thursday, 6 June 2013

Yesterday I wanted to create a box-plot for a small dataset
to see the evolution of 3 stations through a 3 days period. I like box-plots
very much because I think they are one of the clearest ways of showing trend in
your data. R is extremely good for this type of plot and, for this reason, I
decided to add a post on my blog to show how to create a box-plot, but also
because I want to use my own blog to help me remember pieces of code that I
might want to use in the future but that I tend to forget.

For this example I first created a
dummy dataset using the function rnorm() which generates random
normal-distributed sequences. This function requires 3 arguments, the number of
samples to create, the mean and the standard deviation of the distribution, for
example:

rnorm(n=100,mean=3,sd=1)

This generates 100 numbers (floats to be exact), which have
mean equal to 3 and standard deviation equal to 1.

To generate my dataset I used the following line of code:

data<-data.frame(Stat11=rnorm(100,mean=3,sd=2),

Stat21=rnorm(100,mean=4,sd=1),

Stat31=rnorm(100,mean=6,sd=0.5),

Stat41=rnorm(100,mean=10,sd=0.5),

Stat12=rnorm(100,mean=4,sd=2),

Stat22=rnorm(100,mean=4.5,sd=2),

Stat32=rnorm(100,mean=7,sd=0.5),

Stat42=rnorm(100,mean=8,sd=3),

Stat13=rnorm(100,mean=6,sd=0.5),

Stat23=rnorm(100,mean=5,sd=3),

Stat33=rnorm(100,mean=8,sd=0.2),

Stat43=rnorm(100,mean=4,sd=4))

This line creates a data.frame with 12 columns that looks
like this:

Stat11

Stat21

Stat31

Stat41

Stat12

Stat22

Stat32

Stat42

Stat13

Stat23

Stat33

Stat43

5

2

9

-3

10

4

1

1

4

1

5

9

6

13

8

3

7

3

10

10

10

5

9

8

4

4

6

0

10

6

7

6

6

8

2

7

6

7

6

3

9

1

7

0

1

0

6

0

0

2

8

1

6

8

0

8

3

10

9

8

0

19

10

0

11

10

5

6

5

8

10

1

7

4

5

-5

7

0

3

5

2

5

5

3

4

12

9

-4

7

1

9

0

7

2

1

7

7

3

9

0

11

0

8

1

7

0

7

7

6

19

8

3

10

10

9

6

0

2

8

2

6

13

6

-5

12

8

1

4

0

4

5

10

8

11

6

-1

11

4

4

1

4

6

6

10

8

13

5

-5

7

10

0

4

2

7

3

1

2

8

5

-2

5

7

4

2

7

0

3

1

8

11

7

3

11

1

0

9

2

3

5

8

4

19

5

-1

11

6

3

4

9

5

9

0

2

9

5

-3

12

7

6

4

8

2

6

8

7

10

5

-4

8

9

6

9

1

4

3

4

…

…

…

…

…

…

…

…

…

…

…

…

As I mentioned before, this should represent 4 stations for
which the measure were replicated in 3 successive days.

Now, for the creation of the box-plot the simplest function
is boxplot() and can be simply called by adding the name of the dataset as only
argument:

boxplot(data)

This creates the following plot:

It is already a good plot, but it needs some adjustments. It is in black and white, the
box-plots are evenly spaced, even though they are from 3 different replicates,
there are no labels on the axis and the names of the stations are not all
reported.

So now we need to start doing some tweaking.

First, I want to draw the names of the stations vertically,
instead of horizontally. This can be easily done with the argument las. So now the call to the function boxplot()
becomes:

boxplot(data, las =2)

This generates the following plot:

Next, I want to change the name of the stations so that they
look less confusing. For doing that I can use the option names:

Here I am specifying that I want the first 4 box-plots at
position x=1, x=2, x=3 and x=4, then I want to leave a space between the fourth
and the fifth and place this last at x=6, and so on.

If you want to add colours to your box plot, you can use the
option col and specify a vector with the colour numbers or the colour names.
You can find the colour numbers here, and the colour names here.

I just added the two arguments highlighted, but the result
is not what I was expecting

As you can see from the image above, the label on the Y axis
is place very well and we can keep it. On the other hand, the label on the X
axis is drawn right below the stations names and it does not look good.

To solve this is better to delete the option xlab from the
boxplot call and instead use an additional function called mtext(), that places
a text outside the plot area, but within the plot window. To place text within
the plot area (where the box-plots are actually depicted) you need to use the
function text().

The function mtext() requires 3 arguments: the label, the
position and the line number.

An example of a call to the function mtext is the following:

mtext(“Label”, side = 1, line = 7)

the option side takes an integer between 1 and 4, with these
meaning: 1=bottom, 2=left, 3=top, 4=right

The option line takes an integer with the line number,
starting from 0 (which is the line closer to the plot axis). In this case I put
the label onto the 7th line from the X axis.